Switching learning law for differential neural observer for biodegradation process

R. Fuentes, A. García, A. Cabrera, T. Poznyak, I. Chairez

Resultado de la investigación: Contribución a una conferenciaArtículo

Resumen

In this paper, it is presented a differential neural network supplied with a new learning law based on the sliding mode approach. The state observer is employed to estimate the dynamics states of degradation mathematical model, where the incomplete information and the limited on-line measure problems are considered. A new training method is applied in the learning algorithm is proposed to reconstruct Biomass, Organic Matter Recalcitrant concentrations and Volume of biological culture evolutions. This allows ensuring an upper bound for the weights time evolution. This new scheme gives the possibility to construct not only one adaptive process but a set of learning laws. The effectiveness of this algorithm is shown by numerical results. © 2006 IEEE.
Idioma originalInglés estadounidense
Páginas4484-4490
Número de páginas4034
EstadoPublicada - 1 dic 2006
EventoIEEE International Conference on Neural Networks - Conference Proceedings -
Duración: 1 dic 2007 → …

Conferencia

ConferenciaIEEE International Conference on Neural Networks - Conference Proceedings
Período1/12/07 → …

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  • Citar esto

    Fuentes, R., García, A., Cabrera, A., Poznyak, T., & Chairez, I. (2006). Switching learning law for differential neural observer for biodegradation process. 4484-4490. Papel presentado en IEEE International Conference on Neural Networks - Conference Proceedings, .